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Spatial Subgroup Discovery Applied to the Analysis of Vegetation Data

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Practical Aspects of Knowledge Management (PAKM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2569))

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Abstract

We explore the application of Spatial Data Mining, the partially automated search for hidden patterns in georeferenced databases, to the analysis of ecological data. A version of the subgroup mining algorithm is described that searches for deviation patterns directly in a spatial database, automatically incorporating spatial information stored in a GIS into the hypothesis space of a data mining search. We discuss results obtained on a multi-relational biodiversity data set recorded in Niger. Vegetation records are georeferenced and associated with a set of environmental parameters. Further data provide information on climate, soil conditions, and location of spatial objects like rivers, streets and cities. The subgroup mining finds dependencies of a plant species on other species, on local parameters and non-local environmental parameters.

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Author information

Authors and Affiliations

  1. Fraunhofer Institute for Autonomous Intelligent Systems Knowledge Discovery Team, Schloss Birlinghoven, D-53754, Sankt Augustin, Germany

    Michael May & Lemonia Ragia

Authors
  1. Michael May
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  2. Lemonia Ragia
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Editor information

Editors and Affiliations

  1. Department of Knowledge Engineering, University of Vienna, Brünner Str. 72, 1210, Vienna, Austria

    Dimitris Karagiannis

  2. Business Operation Systems, Esslenstr. 3, 8280, Kreuzlingen, Switzerland

    Ulrich Reimer

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© 2002 Springer-Verlag Berlin Heidelberg

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May, M., Ragia, L. (2002). Spatial Subgroup Discovery Applied to the Analysis of Vegetation Data. In: Karagiannis, D., Reimer, U. (eds) Practical Aspects of Knowledge Management. PAKM 2002. Lecture Notes in Computer Science(), vol 2569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36277-0_6

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  • DOI: https://doi.org/10.1007/3-540-36277-0_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00314-4

  • Online ISBN: 978-3-540-36277-7

  • eBook Packages: Springer Book Archive

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